High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

作者：Huichun Zhang, Yufeng Ge, Xinyan Xie, Abbas Atefi, Nuwan K. Wijewardane, Suresh Thapa · 发表于：Plant Methods · 年份：2022 · DOI：10.1186/s13007-022-00892-0 · 被引用次数：123 · 研究领域：Remote Sensing in Agriculture、Smart Agriculture and AI、Spectroscopy and Chemometric Analyses

Abstract Background Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results The models with a single color feature from RGB images predicted chlorophyll content with R 2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R 2 ranging from 0.77 to 0.78. The model using the fluorescence int...